Determining an item's dimensions is often necessary as part of a logistics process (e.g., shipping, storage, etc.). Physically measuring objects, however, is time consuming and may not result in accurate measurements. For example, in addition to human error, measurement errors may result when measuring irregularly shaped objects or when combining multiple objects into a single measurement. As a result, dimensioning systems have been developed to automate, or assist with, this measurement.
A dimensioning system typically senses an object's shape/size in three-dimensions (3D) and then uses this 3D information to compute an estimate of an object's dimensions (e.g., volume, area, length, width, height, etc.). In addition, for irregular objects (or multiple objects), the dimensioning system may compute the dimensions of a minimum bounding box (MVBB) that contains the object (or objects).
The dimensioning system may sense an object by projecting a light pattern (i.e., pattern) into a field-of-view. Objects within the field-of-view will distort the appearance of the light pattern. The dimensioning system can capture an image of the reflected light-pattern and analyze the pattern distortions in the captured image to compute the 3D data necessary for dimensioning.
Accurate dimensioning requires images with (i) high pattern visibility and (ii) high pattern density. In some cases, however, the pattern is hard to resolve. For example, the pattern may be obscured by the shape of the object, or by the object's color (i.e., reflectivity). In other cases, the lighting in the environment may obscure the pattern in the captured images (e.g., under exposure or over exposure). In still other cases, the object may be larger than the dimensioning system's field-of-view. While moving the dimensioning system away from the object may help fit the object within the field-of-view, this comes at the expense of pattern density because the projected pattern spreads as the range between the object and the dimensioning system is increased.
In digital photography image-stitching is the process of combining images to produce a larger, high-resolution image. Image-stitching may be applied to dimensioning in order to increase the dimensioning system's field-of-view without sacrificing pattern density. In addition, image-stitching can help to resolve a pattern that was obscured in a single image. Therefore, a need exists for image-stitching images acquired by a dimensioning system in order to better measure objects.